关于北京市政府支出与居民消费的研究

利用北京市城乡居民1985年～2005年的人均消费、政府支出和可支配收入等数据,以跨期替代的居民消费模型为基础,通过建立协整方程和误差修正模型对政府支出如何影响居民消费进行了实证研究。研究发现,政府支出对居民消费支出具有挤入效应,且挤入效应在长期内明显强于短期;决定城乡居民消费水平最重要的因素是可支配收入的增加;农村居民消费对政府支出的敏感性比城镇居民低,其消费更加依赖于可支配收入的增长。     

四川省耕地资源变化与经济增长的计量分析

应用时间序列分析方法,采用四川省1978～2009年的耕地资源、GDP数据对四川省耕地面积与经济增长之间的关系进行了定量分析。结果表明,四川耕地面积与经济增长之间具有长期均衡关系,四川GDP增长1%,将导致耕地面积减少0.021%,但当耕地面积减少偏离均衡太大时,会有一个强有力的修正行为,误差修正系数为-1.2024。研究表明,耕地面积会趋于达到长期均衡状态,但这种平衡的获得需要政府部门进一步加强对耕地的保护,特别是要加强对建设占用耕地的监督和管理。     

分数布朗运动下B-S权证定价模型的修正

分析证券市场的有效性,指出其线性范式与现实市场状况并不符合。传统的金融学认为证券收益率服从对数正态分布,而大量的实证表明收益率分布与正态分布相比有＂尖峰胖尾＂特征,具有分形结构。在此基础上剖析了传统B-S权证定价模型的不足,结合分形市场中的分数布朗运动,提出了基于分形理论的B-S股本权证定价模型,考虑了股本稀释效应。由于股本权证定价模型需要已知企业股权价值及其波动率,但企业价值是权证价格的函数。基于此,运用数值方法以股票价格和波动率来估计企业价值波动率,并给出了在实际运用中的案例。     

中国城乡居民收入差距影响因素分析

目前,过大的城乡收入差距已成为中国经济发展的重要瓶颈,影响了社会的公正与稳定,故将城乡二元结构系数、外贸依存度以及城镇居民人均可支配收入发展速度作为城乡居民收入差距变动的影响变量,通过建立协整回归和误差修正模型得到反映它们之间长期均衡与短期波动的表达式。实证分析结果表明：城镇居民人均可支配收入发展速度是影响中国城乡居民收入差距变动的主要因素。     

An interchange algorithm for four factor orthogonal main effect plans

In this paper we give a construction for four factor orthogonal main effect plans (OMEPs) and an interchange algorithm to give four factor OMEPs with various different numbers of repeated runs.     

中国能源消费与经济增长之间的关系研究——以石油资源为例

能源是人类社会存在和发展的先决条件,是国民经济增长的重要物质基础。随着中国经济的迅速崛起,能源消费量不断增长。本文在对中国能源消费状况进行深入分析的基础上,运用协整理论和Granger因果关系检验理论,对中国石油消费和经济增长之间的关系进行了实证研究。结论表明,中国石油消费与经济增长之间存在正相关关系和单向因果关系。     

Basis risk modelling: a cointegration-based approach

ABSTRACT

In this paper we propose a multivariate approach for forecasting pairwise mortality rates of related populations. The need for joint modelling of mortality rates is analysed using a causality test. We show that for the datasets considered, the inclusion of national mortality information enhances predictions on its subpopulations. The investigated approach links national population mortality to that of a subset population, using an econometric model that captures a long-term relationship between the two mortality dynamics. This model does not focus on the correlation between the mortality rates of the two populations, but rather their long-term behaviour, which suggests that the two times series cannot wander off in opposite directions for long before mean reverting, which is consistent with biological reasoning. The model can additionally capture short-term adjustments in the mortality dynamics of the two populations. An empirical comparison of the forecast of one-year death probabilities for policyholders is performed using both a classical factor-based model and the proposed approach. The robustness of the model is tested on mortality rate data for England and Wales, alongside the Continuous Mortality Investigation assured lives dataset, representing the subpopulation.     

Objective Bayes Covariate‐Adjusted Sparse Graphical Model Selection

We present an objective Bayes method for covariance selection in Gaussian multivariate regression models having a sparse regression and covariance structure, the latter being Markov with respect to a directed acyclic graph (DAG). Our procedure can be easily complemented with a variable selection step, so that variable and graphical model selection can be performed jointly. In this way, we offer a solution to a problem of growing importance especially in the area of genetical genomics (eQTL analysis). The input of our method is a single default prior, essentially involving no subjective elicitation, while its output is a closed form marginal likelihood for every covariate‐adjusted DAG model, which is constant over each class of Markov equivalent DAGs; our procedure thus naturally encompasses covariate‐adjusted decomposable graphical models. In realistic experimental studies, our method is highly competitive, especially when the number of responses is large relative to the sample size.     

Inference for impulse response coefficients from multivariate fractionally integrated processes

This article considers a multivariate system of fractionally integrated time series and investigates the most appropriate way for estimating Impulse Response (IR) coefficients and their associated confidence intervals. The article extends the univariate analysis recently provided by Baillie and Kapetanios (2013 Baillie, R. T., Kapetanios, G. (2013). Estimation and inference for impulse response functions form univariate strongly persistent processes. Econometrics Journal 16:373–399.[Crossref], [Web of Science ®] , [Google Scholar]), and uses a semiparametric, time domain estimator, based on a vector autoregression (VAR) approximation. Results are also derived for the orthogonalized estimated IRs which are generally more practically relevant. Simulation evidence strongly indicates the desirability of applying the Kilian small sample bias correction, which is found to improve the coverage accuracy of confidence intervals for IRs. The most appropriate order of the VAR turns out to be relevant for the lag length of the IR being estimated.     

分数阶微分的一些性质

近300年来,分数阶微积分这一重要数学分支渐成体系,它被应用于许多学科的工程计算中,特别是在化学,电磁学,控制学,材料学和力学中.分数阶微积分的定义有各种不同形式,文章给出了一种重要的分数阶微分定义——Caputo分数阶微分,且针对这种分数阶微分的定义研究了它的一些性质.